For a city, analyzing its advantages, disadvantages and the level of economic development in a country is important, especially for the cities in China developing at flying speed. The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail. In this paper, based on multiple indicators of economy and industry, the urban hierarchical structure of 285 cities above the prefecture level in China is investigated. The indicators from the economy, industry, infrastructure, medical care, population, education, culture, and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure. The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China. According to the comprehensive scores, 285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm. Then, the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed. The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries. The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.
For a country, the development level and perfection of infrastructure and transportation vary greatly among the cities, especially for the cities in China developing at flying speed. It is very necessary to analyze the hierarchical structure of the cities for urban construction improvement, urban planning, urban structure optimization and economic development. The extant literatures mainly focused on urban competitiveness and sustainable development in China and the countries around the world, and few studies pay attention to the comprehensive or integrated development or pattern of the city. To fill this gap, this paper investigates the hierarchical structure of the cities based on the urban comprehensive development and pattern with the indicators of economy and industry in detail, and more cities are involved to offer more references for the current overall development of the cities in a country.
The hierarchical structure of the cities can be studied by evaluating the urban development. Urban competitiveness and urban sustainability are used to evaluate urban development from a specific perspective. About urban competitiveness, Jiang et al. [
About the evaluation of urban sustainability, Li et al. [
For other aspects of city evaluation, Shan et al. [
From extant literatures, most researches focused on the urban competitiveness and sustainable development in China and the countries around the world, and few studies pay attention to the comprehensive or integrated development or pattern of the city. For the indicator system, the selected indicators concentrate more on the perspective of urban competitiveness and sustainable development. There are few cities involved in relevant researches, which has less reference for the current overall development of the cities in a country. The application scope of the existing researches is limited to some regions in a country, or to measure the comprehensive competitiveness or sustainability of the cities. In addition, there are few researches investigating the urban hierarchical structure according to the comprehensive and overall urban development or pattern. China is a developing country, and there are many prefecture-level cities. There are big gaps among these cities in urban development. With the rapid development of cities above the prefecture level in China in recent years, it is particularly important to analyze the hierarchical structure of the cities above the prefecture level in China according to the current comprehensive and overall urban development.
In this paper, the indicators, including eight categories and a total of 33 indicators, of urban hierarchical structure analysis are selected. Factor analysis and K-means clustering algorithm are used to investigate the hierarchical structure of 285 prefecture level cities in China. More cities in China are involved in this paper. The results can give a more accurate understanding of the overall development trend of the cities in China, and offer a reference for the development of prefecture level cities in China at the present stage. The results and implications can not only be applied to the urban planning and development in China, but also offer a reference on other developing countries. The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.
The contribution of this paper is that: (1) Based on the comprehensive development or pattern of the city, the indicators from the aspects of economy, industry, infrastructure, medical care, population, education, culture and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure; (2) More cities, including 285 cities, are involved to offer more references for the current overall development of the cities in China; (3) Factor analysis method and K-means clustering algorithm are used to investigate the relationship between the variables of selected indicators, obtain the score of each common factor and comprehensive scores and rankings for 285 cities above prefecture level in China, and classify the 285 cities into 15 levels; (4) The hierarchical structure system of the cities above prefecture level in China is obtained and corresponding policy implications are proposed.
In this paper, the urban hierarchical structure of 285 cities above prefecture level in China is explored by using data analysis [
By forming a new indicator system considering the aspects of economy, industry, infrastructure, medical care, population, education, culture and employment levels and collecting the corresponding data, the data system of urban hierarchical structure analysis is proposed. Then factor analysis method and K-means clustering algorithm are used to analyze the data system.
Factor analysis method is the one to explore the relationship between the selected variables of indicators, extract the common factors of the variables, and calculate the factor score coefficients. K-means clustering algorithm is a method that
In this paper, factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above prefecture level in China. K-means clustering algorithm is used to cluster 285 cities above prefecture level into 15 levels according to the comprehensive scores from factor analysis. Then, the hierarchical structure system of the cities above prefecture level in China is obtained.
According to the analysis of the results and the hierarchical structure system of the cities above prefecture level in China, the corresponding policy implications are proposed.
The methodology in this paper is shown in
Based on the literature review, the indicators from the aspects of economy, industry, infrastructure, medical care, population, education, culture and employment levels are selected comprehensively to establish a new indicator system for analyzing urban hierarchical structure according to the comprehensive development and pattern of the cities.
Indicator | References | Mean | Standard deviation | Min | Max |
---|---|---|---|---|---|
GDP ( |
Jiang et al. [ |
2.88e11 | 3.86e11 | 2.1e10 | 3.06e12 |
Average wages of employees ( |
Jiang et al. [ |
63405.17 | 12543.12 | 36703.33 | 143030 |
FDI ( |
Jiang et al. [ |
6.63e9 | 1.56e10 | 0 | 1.64e11 |
Fixed asset investment ( |
Jiang et al. [ |
2.06e11 | 2.02e11 | 5.59e9 | 1.74e12 |
Government fiscal deficit ( |
Jiang et al. [ |
2.17e10 | 1.91e10 | −1.37e10 | 2.08e11 |
|
|||||
Added value of secondary industry ( |
Jiang et al. [ |
1.25e11 | 1.51e11 | 5.64e9 | 9.33e11 |
Added value of tertiary industry ( |
Jiang et al. [ |
1.46e11 | 2.56e11 | 9.21e9 | 2.26e11 |
Total industrial assets ( |
Zhen et al. [ |
3.58e11 | 5.34e11 | 1.35e9 | 4.6e12 |
Main industrial operating income ( |
Jiang et al. [ |
3.8e11 | 5.07e11 | 3.04 e9 | 3.79e13 |
Number of industrial enterprises ( |
New indicator | 1247.33 | 1514.6 | 20 | 9840 |
Number of industrial employees ( |
New indicator | 286429.6 | 407912.6 | 4764 | 3175313 |
|
|||||
Per capita paved road area ( |
Jiang et al. [ |
5.909 | 4.908 | 0.404 | 38.458 |
Per capita park greening curbrace¿ area ( |
Jiang et al. [ |
5.443 | 5.38 | 0.382 | 53.182 |
Number of beds in hospitals or health centers ( |
Jiang et al. [ |
25605.64 | 22122.14 | 2040 | 206080 |
|
|||||
Resident population ( |
Du et al. [ |
4517665 | 3476147 | 249800 | 3.08e7 |
|
|||||
Number of students in colleges and universities ( |
Jiang et al. [ |
98898.07 | 173445.6 | 0 | 1067335 |
|
|||||
Number of books in public libraries ( |
Jiang et al. [ |
3056057 | 6298804 | 157000 | 7.77e7 |
Number of museums ( |
New indicator | 16.414 | 22.794 | 1 | 243 |
Number of cultural centers ( |
New indicator | 10.877 | 16.443 | 1 | 266 |
|
Du et al. [ |
||||
Number of employees of wholesale and retail ( |
Jiang et al. [ |
33552.95 | 89429.2 | 895 | 864470 |
Number of employees of transportation, warehousing and post ( |
New indicator | 27504.37 | 62015.15 | 1139 | 576935 |
Number of employees of accommodation and catering ( |
New indicator | 11338.15 | 35767.88 | 96 | 396820 |
Number of employees of information transmission, computer service and software ( |
New indicator | 14412.54 | 56320.31 | 400 | 774400 |
Number of employees of finance ( |
Jiang et al. [ |
23301.85 | 43225.92 | 1784 | 544498 |
Number of employees of real estate ( |
Jiang et al. [ |
15735.17 | 39364.39 | 264 | 442784 |
Number of employees of leasing and business service ( |
New indicator | 18986.02 | 68906.1 | 180 | 882695 |
Number of employees of scientific research, technical service and geological exploration ( |
Jiang et al. [ |
14510.08 | 49095.52 | 403 | 712481 |
Number of employees of water resources, environment and public facility management ( |
New indicator | 8914.544 | 10846.1 | 851 | 106640 |
Number of employees of resident service, repair, and other services ( |
New indicator | 4347.544 | 20439.3 | 15 | 293329 |
Number of employees of education ( |
Jiang et al. [ |
56245.89 | 53442.67 | 2708 | 505697 |
Number of employees of health, social security, and social welfare ( |
Jiang et al. [ |
29669.36 | 30453.16 | 2041 | 293252 |
Number of employees of culture, sports and entertainment ( |
Du et al. [ |
5405.123 | 13741.52 | 321 | 190189 |
Number of employees of public management and social organization ( |
Du et al. [ |
53838.74 | 43513.53 | 6176 | 478359 |
Economy
The economic indicators include gross domestic product (GDP), foreign direct investment (FDI), average wages of employees, fixed asset investment and government fiscal deficit.
GDP is an important indicator of urban economic development, which reflects the economic situation and market scale of a city. FDI reflects the external economic strength, competitiveness and influence, as well as the level of opening to the foreign countries. The average wage of employees reflects the national economic level, residents’ consumption ability and the overall economic level of a city. The fixed asset investment is the embodiment of urban capital savings and economic strength. These economic indicators reflect the economic development and foreign economic competitiveness of a city.
Industry
The industrial indicators include added value of the secondary industry, added value of the tertiary industry, total industrial assets, main industrial operating income, number of industrial enterprises and number of industrial employees.
The added values of the secondary and tertiary industries reflect the economic growth of the industries in a certain period of time. The total industrial assets, main operating income, number of industrial enterprises and number of industrial employees are the basic indicators of industrial development. These industrial indicators reflect the changes of urban industrial structure, which are the important factors for the development of urban industrial structure, economic development and urban planning.
Infrastructure
The infrastructure indicators include per capita paved road area and per capita park greening area. These indicators reflect the construction level and completeness level of urban infrastructure, which is necessary for urban development.
Medical care
The medical indicator includes number of beds in hospitals or health centers. This indicator reflects the level of social services and medical care.
Population
The resident population is selected as the population indicator. The population indicator is the basic factor of urban scale and economic development.
Education
The number of students in colleges and universities is considered as the education indicator. The education indicator reflects the quality level of education of a city, which is the basis of scientific and technological innovation and talent reserve.
Culture
The cultural indicators include number of books in public libraries, number of museums and number of cultural centers. These indicators reflect the scale and quality level of cultural facilities and the maturity of urban development.
Employment
The employment indicators include number of employees for the sub-industries of tertiary industry, such as the wholesale and retail, transportation, warehousing and post, accommodation and catering, information transmission, computer service and software, finance, real estate, leasing and business service, scientific research, technical service and geological exploration, water resources, environment and public facility management, resident service, repair, and other services, education, health, social security, and social welfare, culture, sports and entertainment, and public management and social organization.
The employment indicators mainly focus on the number of employees in each sub-industry of the tertiary industry, reflecting the tertiary industry structure and industrial development level. As an important part of the industrial structure, the tertiary industry accounts for a large proportion of the total GDP and is an important driving force for urban economic and industrial development.
From
285 cities above prefecture level in China are considered. These cities include Beijing, Tianjin, Shanghai, Chongqing, and the prefecture-level cities in Hebei Province, Henan Province, Yunnan Province, Liaoning Province, Heilongjiang Province, Hunan Province, Anhui Province, Shandong Province, Jiangsu Province, Zhejiang Province, Jiangxi Province, Hubei Province, Gansu Province, Shanxi Province, Shaanxi Province, Jilin Province, Fujian Province, Guizhou Province, Guangdong Province, Qinghai Province, Sichuan Province, Hainan Province, Inner Mongolia Autonomous Region, Ningxia Hui Autonomous Region, Guangxi Zhuang Autonomous Region and Xinjiang Uygur Autonomous Region. There excludes the cities at the same level in Taiwan Province and Tibet Autonomous Region, as well as Hong Kong Special Administrative Region and Macao Special Administrative Region. The 285 cities above prefecture level can reflect the characteristics and system of urban hierarchical structure in China as a whole.
The data of economic, industrial, infrastructure, population, medical care, education and cultural indicators are collected from the Statistical Yearbook of Municipalities, Provinces and Autonomous Regions in China and the City Statistical Yearbook in 2017. The data of employment indicators are collected from the City Statistical Yearbook in 2017.
Bartlett sphere test and Kaiser-Meyer-Olkin (KMO) test are used to determine whether the data samples are suitable for factor analysis.
Bartlett sphere test can test the correlation between variables and judge whether each variable is independent. By using Stata, the results show that the chi-square statistic is 20747.955, the degree of freedom is 528, and the
KMO test is a method to compare the coefficients of correlation and partial correlation of variables. When the sum square of the coefficient of the correlation between variables is greater than the sum square of the coefficient of the partial correlation, the value of KMO will be closer to 1, indicating that the stronger the correlation between variables is, the more suitable the data are used for factor analysis. By using Stata, the result shows that KMO = 0.946, indicating that the data samples are suitable for factor analysis.
Then, the data samples are used to do the factor analysis. By using Stata, the common factor is extracted and rotated, and the factor load matrix after rotation is calculated. The rotated factor does not change the fitting degree of the model to the data, nor does it change the common factor variance of each variable, which can explain the variables better.
Factor | Difference | Eigenvalue | Proportion | Cumulative |
---|---|---|---|---|
Factor 1 ( |
12.60148 | 5.26087 | 0.3819 | 0.3819 |
Factor 2 ( |
7.34062 | 2.28219 | 0.2224 | 0.6043 |
Factor 3 ( |
5.05843 | 3.10420 | 0.1533 | 0.7576 |
Factor 4 ( |
1.95423 | 0.30668 | 0.0592 | 0.8168 |
Factor 5 ( |
1.64754 | - | 0.0499 | 0.8667 |
Variable | Common factor | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
GDP | 0.6028 | 0.6985 | 0.262 | 0.1264 | 0.083 |
Average wages of employees | 0.4524 | 0.3915 | 0.1568 | 0.4417 | −0.1723 |
FDI | 0.7807 | 0.4264 | 0.2979 | 0.0823 | 0.0741 |
Fixed asset investment | 0.2963 | 0.5503 | 0.6891 | 0.0438 | 0.1019 |
Government fiscal deficit | 0.5201 | 0.1898 | 0.5932 | −0.2039 | −0.1082 |
Added value of secondary industry | 0.3404 | 0.8144 | 0.3893 | 0.1225 | 0.1158 |
Added value of tertiary industry | 0.7162 | 0.5962 | 0.2939 | 0.1209 | 0.0322 |
Total industrial assets | 0.6278 | 0.6854 | 0.2135 | 0.1258 | −0.0129 |
Main industrial operating income | 0.3351 | 0.8859 | 0.2146 | 0.083 | 0.0385 |
Number of industrial enterprises | 0.1529 | 0.914 | 0.1943 | 0.0068 | 0.0791 |
Number of industrial employees | 0.2107 | 0.9158 | 0.1393 | 0.0708 | 0.0665 |
Per capita paved road area | 0.0303 | 0.1309 | −0.0512 | 0.883 | 0.0359 |
Per capita park greening area | 0.1588 | 0.08 | −0.0443 | 0.8309 | 0.0163 |
Number of beds in hospitals or health centers | 0.3994 | 0.4281 | 0.7391 | −0.0721 | 0.1922 |
Resident population | 0.4468 | 0.514 | 0.6678 | −0.145 | 0.082 |
Number of students in colleges and universities | 0.3575 | 0.2908 | 0.6437 | 0.2784 | 0.1948 |
Number of books in public libraries | 0.5802 | 0.6467 | 0.128 | 0.0827 | 0.1016 |
Number of museums | 0.2246 | 0.3791 | 0.3937 | 0.0596 | 0.3687 |
Number of cultural centers | −0.0184 | −0.0901 | 0.5331 | −0.0071 | −0.0785 |
Number of employees of wholesale and retail | 0.783 | 0.3069 | 0.2026 | 0.0287 | 0.4716 |
Number of employees of transportation, warehousing and post | 0.8087 | 0.3262 | 0.3147 | 0.0946 | 0.2765 |
Number of employees of accommodation and catering | 0.774 | 0.2289 | 0.1615 | 0.0395 | 0.5352 |
Number of employees of information transmission, computer service and software | 0.9432 | 0.179 | 0.1159 | 0.0566 | 0.1316 |
Number of employees of finance | 0.8606 | 0.3197 | 0.2939 | 0.0633 | −0.014 |
Number of employees of real estate | 0.8666 | 0.3632 | 0.2269 | 0.0819 | 0.1333 |
Number of employees of leasing and business service | 0.9374 | 0.2807 | 0.1132 | 0.0334 | 0.0815 |
Number of employees of scientific research, technical service and geological exploration | 0.9392 | 0.1462 | 0.2076 | 0.0732 | 0.0196 |
Number of employees of water resources, environment and public facility management | 0.7308 | 0.2932 | 0.5027 | 0.0872 | 0.1153 |
Number of employees of resident service, repair, and other services | 0.5016 | 0.0681 | 0.1357 | 0.0177 | 0.7814 |
Number of employees of education | 0.6368 | 0.3487 | 0.6448 | −0.0505 | 0.0853 |
Number of employees of health, social security, and social welfare | 0.6767 | 0.3922 | 0.5693 | −0.0043 | 0.18 |
Number of employees of culture, sports and entertainment | 0.915 | 0.1408 | 0.2539 | 0.0676 | 0.1721 |
Number of employees of public management and social organization | 0.684 | 0.317 | 0.5894 | −0.0661 | 0.0181 |
From
Based on
The first common factor reflects the level of employment and economy. It better explains following variables, including the number of employees of information transmission, computer service and software, scientific research, technical service and geological exploration, leasing and business service, culture, sports and entertainment, real estate, finance, transportation, warehousing and post, wholesale and retail, accommodation and catering, water resources, environment and public facility management, public management and social organization, health, social security, and social welfare, and education, FDI, added value of tertiary industry, and average wages of employees.
The second common factor reflects the industrial level of a city. The variables include the number of industrial employees, number of industrial enterprises, main industrial operating income, added value of secondary industry, GDP, total industrial assets, and number of books in public libraries.
The third common factor reflects the level of medical care, population, education and culture of a city. The variables include the number of beds in hospitals or health centers, investment in fixed assets, resident population, the number of students in colleges and universities, government fiscal deficit and the number of cultural centers. The third common factor of the absolute values of the correlation coefficients for the number of museums is the largest, thus relatively speaking, it better explains this variable.
The fourth common factor reflects the level of infrastructure of a city, which includes per capita paved roads and per capita park greening area.
The fifth common factor reflects the employment level of a city, involving the number of employees in resident service, repair, and other services.
Using Stata, the coefficients of the factor scores of common factors are obtained. The factor score models are established based on the coefficients of the factor scores of common factors obtained in
where
The common factor scores and ranking of 285 cities in China are calculated through formulas
Taking the proportion of common factors as the weights, the comprehensive scores and ranking of 285 cities above prefecture level in China are calculated. The formula is
where the coefficients of the variables are the proportion of the first to fifth common factors obtained in
By calculation,
City | Ranking | Comprehensive score ( |
|||||
---|---|---|---|---|---|---|---|
Overall | |||||||
Beijing | 1 | 1 | 284 | 104 | 188 | 285 | 4.80628 |
Shanghai | 2 | 2 | 3 | 282 | 262 | 4 | 3.32381 |
Chengdu | 3 | 3 | 279 | 14 | 142 | 1 | 1.99311 |
Shenzhen | 4 | 4 | 2 | 285 | 149 | 284 | 1.85197 |
Guangzhou | 5 | 5 | 13 | 9 | 14 | 163 | 1.7661 |
Chongqing | 6 | 40 | 16 | 1 | 222 | 282 | 1.72174 |
Tianjin | 7 | 6 | 10 | 8 | 23 | 281 | 1.45637 |
Hangzhou | 8 | 7 | 11 | 53 | 42 | 31 | 1.06853 |
Suzhou (Jiangsu Province) | 9 | 282 | 1 | 283 | 112 | 59 | 0.93134 |
Wuhan | 10 | 116 | 20 | 6 | 22 | 8 | 0.84777 |
Nanjing | 11 | 9 | 38 | 15 | 7 | 14 | 0.81181 |
Xi’an | 12 | 8 | 208 | 7 | 29 | 6 | 0.72164 |
Zhengzhou | 13 | 135 | 29 | 4 | 46 | 139 | 0.69698 |
Qingdao | 14 | 244 | 14 | 19 | 38 | 34 | 0.58333 |
Changsha | 15 | 97 | 22 | 11 | 66 | 270 | 0.54257 |
Ningbo | 16 | 269 | 6 | 128 | 83 | 171 | 0.50498 |
Jinan | 17 | 14 | 40 | 31 | 24 | 246 | 0.49688 |
Changchun | 18 | 16 | 54 | 17 | 54 | 249 | 0.46747 |
Harbin | 19 | 24 | 272 | 3 | 37 | 48 | 0.45584 |
Wuxi | 20 | 278 | 7 | 209 | 59 | 58 | 0.40059 |
City | GDP (1011 RMB) | FDI (1011 RMB) | Secondary industry (1011 RMB) | Tertiary industry (1011 RMB) | Per capita paved road | Resident population (106) | Beds in hospitals/ health centers (104) | Students in colleges/ universities (104) | Books in public libraries (106) | Total employment (106) |
Beijing | 28.01 | 1.64 | 5.33 | 22.57 | 4.77 | 21.71 | 11.37 | 58.07 | 27.59 | 6.67 |
Shanghai | 30.63 | 1.15 | 9.33 | 21.19 | 12.34 | 24.18 | 13.46 | 51.49 | 77.73 | 4.22 |
Chengdu | 13.89 | 0.68 | 6 | 7.39 | 6.72 | 16.04 | 13.45 | 81.74 | 15.5 | 3.88 |
Shenzhen | 22.49 | 0.5 | 9.32 | 13.15 | 9.7 | 12.52 | 3.98 | 9.67 | 40.70 | 1.97 |
Guangzhou | 21.5 | 0.42 | 6.01 | 15.27 | 8.98 | 14.49 | 9.02 | 106.73 | 29.86 | 2.22 |
Chongqing | 60.39 | 0.69 | 8.6 | 9.56 | 6.78 | 30.75 | 20.61 | 80.52 | 16.72 | 2.13 |
Tianjin | 18.6 | 0.72 | 7.59 | 10.79 | 9.47 | 15.57 | 6.02 | 51.47 | 16.22 | 1.52 |
Hangzhou | 12.6 | 0.45 | 4.36 | 7.93 | 8.76 | 9.47 | 7.59 | 42.58 | 22.81 | 1.48 |
Suzhou(Jiangsu Province) | 17.32 | 0.3 | 8.24 | 8.86 | 10.23 | 10.68 | 6.66 | 22.37 | 22.18 | 0.79 |
Wuhan | 13.41 | 0.61 | 5.86 | 7.14 | 9.5 | 10.89 | 9.16 | 95.77 | 16.27 | 0.12 |
Nanjing | 11.72 | 0.25 | 4.45 | 7 | 18.31 | 8.34 | 5.22 | 84.08 | 7.01 | 0.11 |
Xi’an | 7.47 | 0.36 | 2.6 | 4.59 | 10.22 | 9.62 | 6.12 | 72.68 | 2.27 | 0.13 |
Zhengzhou | 9.19 | 0.27 | 4.08 | 4.96 | 5.83 | 9.98 | 9.15 | 93.53 | 3.15 | 0.99 |
Qingdao | 11.02 | 0.52 | 4.31 | 6.11 | 9.14 | 9.29 | 5.58 | 34.6 | 7 | 0.71 |
Changsha | 10.21 | 0.35 | 4.74 | 5.16 | 6.12 | 7.92 | 7.37 | 61.04 | 9.66 | 0.66 |
Ningbo | 9.84 | 0.27 | 5.12 | 4.42 | 8.94 | 8.01 | 3.73 | 15.61 | 8.17 | 0.63 |
Jinan | 7.15 | 0.13 | 2.57 | 4.31 | 12.87 | 7.32 | 5.49 | 54.44 | 1.36 | 0.77 |
Changchun | 6.5 | 0.5 | 3.17 | 3.04 | 9.48 | 7.51 | 4.99 | 43.78 | 5.22 | 0.68 |
Harbin | 6.26 | 0.23 | 1.82 | 3.84 | 9.86 | 9.55 | 8.19 | 62.87 | 5.77 | 0.86 |
Wuxi | 10.51 | 0.25 | 4.96 | 5.41 | 10.71 | 6.55 | 4.32 | 11.27 | 7.88 | 0.4 |
According to the comprehensive scores of 285 cities obtained by factor analysis, K-means clustering algorithm method is used for clustering analysis, and the hierarchical structure of the cities above prefecture level in China is established, as shown in
For the levels of the cities, 8 to 15 levels are computed by using K-means clustering algorithm method, respectively. Comparing the results of these levels, we select 15 levels in this paper. For 8 to 14 levels, the results show that these levels are not very detailed and specific, because some levels include too many cities, which cannot show the clear changes and differences among the cities in different levels. Thus, for 285 cities, 15 levels are considered in this paper to show the differences between different levels of the cities from 8 aspects and the characteristics and development level of the cities more specifically and clearly.
From
Level | City | Mean value | |||||
---|---|---|---|---|---|---|---|
1 | Beijing, Shanghai | 10.046 | 1.783 | −0.591 | −0.736 | −0.681 | 4.065 |
2 | Chengdu, Shenzhen, Guangzhou, Chongqing, Tianjin | 1.988 | 2.265 | 2.438 | 0.368 | 1.987 | 1.758 |
3 | Hangzhou, Suzhou (Jiangsu Province), Wuhan, Nanjing, Xi’an, Zhengzhou | 0.258 | 1.901 | 1.497 | 1.191 | 0.499 | 0.846 |
4 | Qingdao, Changsha, Ningbo, Jinan, Changchun, Harbin, Wuxi, Fuzhou (Fujian Province), Dongguan, Shenyang, Hefei, Foshan, Xiamen, Dalian, Shijiazhuang, Kunming | −0.064 | 1.124 | 0.857 | 1.016 | −0.008 | 0.417 |
5 | Nantong, Yantai, Nanning, Nanchang, Taiyuan, Wenzhou, Quanzhou, Weifang, Tangshan, Xuzhou, Changzhou, Guiyang, Linyi, Zhuhai | −0.296 | 0.892 | 0.615 | 0.508 | −0.148 | 0.202 |
6 | Jiaxing, Urumqi, Luoyang, Lanzhou, Lu’an, Nanyang, Jining, Huizhou, Yancheng, Langfang, Zibo, Taizhou, Jinhua, Baoding, Xiangyang, Shaoxing, Yangzhou, Taizhou, Ganzhou | −0.193 | 0.436, | 0.302, | −0.089 | 0.238 | 0.076 |
7 | Yichang, Dongying, Ordos, Hohhot, Daqing, Zhenjiang, Heze, Zunyi, Cangzhou, Dezhou, Weihai, Shangqiu, Haikou, Huai’an, Wuhu, Zhangzhou, Handan, Liaocheng, Yinchuan, Tai’an, Liuzhou, Lianyungang, Baotou, Jiangmen, Yulin, Zhumadian, Zhongshan, Hengyang, Zhoukou, Zhanjiang | −0.179 | 0.061 | 0.269 | 0.115 | −0.186 | −0.016 |
8 | Jiujiang, Zhuzhou, Shantou, Quzhou, Jinzhong, Mianyang, Huzhou, Yueyang, Xinyang, Binzhou, Jilin, Xingtai, Changde, Guilin, Chenzhou, Huanggang, Maoming, Weinan, Qinhuangdao, Nanchong, Shangrao, Zhangjiakou, Luliang, Xuchang, Lishui, Xining, Changzhi, Xinxiang, Qiqihar, Xianyang, Pingdingshan, Xiaogan, Zhoushan | −0.123 | −0.174 | 0.117 | −0.163 | −0.112 | −0.083 |
9 | Linfen, Shiyan, Yuncheng, Shaoyang, Yichun, Jiaozuo, Fuyang, Zhaoqing, Karamay, Longyan, Sanming, Suqian, Chifeng, Kaifeng, Ma’anshan, Qingyuan, Yiyang, Rizhao, Zaozhuang, Xiangtan, Qujing, Ji’an, Yongzhou, Datong, Jingzhou, Putian, Anyang, Jieyang, Anshan, Chengde | −0.134 | −0.157 | −0.076 | −0.253 | −0.107 | −0.118 |
10 | Jincheng, Ningde, Bengbu, Anqing, Chuzhou, Huainan, Meizhou, Dazhou, Nanping, Jiayuguan, Hulunbuir, Yulin, Baoji, Mudanjiang, Yibin, Yan’an, Zhaotong, Jingmen, Shaoguan, Liupanshui, Deyang, Heihe, Huaihua, Fuzhou (Jiangxi Province), Suzhou (Anhui Province), Wuhai, Hanzhong, Sanya, Puyang, Xinzhou | −0.125 | −0.371 | −0.167 | 0.072 | −0.004 | −0.152 |
11 | Hengshui, Luzhou, Yuxi, Jingdezhen, Tongliao, Wulanchabu, Baise, Xuancheng, Mazhou, Yingkou, Loudi, Leshan, Panzhihua, Sanmenxia, Hechi, Lijiang, Anshun, Pu’er, Jinzhou, Songyuan, Shizuishan, Shuozhou, Heyuan, Huaibei, Jiamusi, Suihua, Yangjiang, Luohe, Huangshi, Guang’an, Fushun, Siping | −0.048 | −0.406 | −0.408 | −0.198 | −0.076 | −0.187 |
12 | Chaoyang, Wuzhou, Guigang, Tongling, Bayannur, Zhangye, Ankang, Zigong, Qinzhou, Tianshui, Yangquan, Beihai, Lincang, Laiwu, Tonghua, Guangyuan, Huangshan, Panjin, Shuangyashan, Liaoyang, Guyuan, Qingyang, Pingxiang, Zhongwei, Xinyu, Yingtan, Baicheng, Meishan, Yunfu, Fuxin, Baoshan | −0.058 | −0.456 | −0.543 | −0.12 | −0.037 | −0.216 |
13 | Huludao, Neijiang, Benxi, Bazhong, Chaozhou, Dandong, Suining, Fangchenggang, Jixi, Dingxi, Wuzhong, Wuwei, Hezhou, Laibin, Pingliang, Chizhou, Chongzuo, Zhangjiajie, Tieling, Jinchang, Jiuquan, Baiyin, Shanwei, Ziyang, Hebi, Yichun, Xianning, Tongchuan, Baishan | −0.056 | −0.491 | −0.584 | −0.314 | 0.024 | −0.237 |
14 | Longnan, Shangluo, Hegang, Ezhou, Suizhou, Liaoyuan | −0.066 | −0.464 | −0.724 | −0.45 | 0.207 | −0.256 |
15 | Qitaihe, Ya’an | −0.101 | −0.565 | −0.762 | 0.019 | 0.202 | −0.27 |
From
The cities with the highest
For level of medical care, population, education and culture, Chongqing stands the first place in the medical treatment for the number of beds in hospitals or health centers and resident population, reaching 206,080 and 30.75 million people. Concerning the aspect of culture, the number of museum is relatively high with 94.
With the regards to infrastructure, the per capita paved road area in Xaimen is 13.59, which is relatively complete than other cities. Its per capita park greening area is obviously high with 53.18, which shows dominate in 285 cities. There is a comfortable environment with large green area and many scenic spots, such as Kulangsu, with beatiful scenery in this city, which creats it a national ecological garden city.
Among the top 20 cities, the cities with the highest
For the bottom 20 cities, their scores of
From
Level 2 is the cities with strong comprehensive strength. There is a gap between the comprehensive score of the cities in Level 2 and Level 1 due to the large differences of the value of
Levels 3 to 6 are the potential cities with positive comprehensive scores, and their development are above the average level. The cities in Level 3 show balanced development with the positive scores of all five common factors, and have better development in industry. There is a lack of development in some aspects for the cities in Levels 4 to 6 due to the negative scores of some common factors, and these cities need to improve the economy and employment level. The development of economy, employment and infrastructure for the cities in Level 6 lags behind. Without changing their structural characteristics and environment, these cities can make up for slightly backward aspects according to their own needs for development, which can make more complete urban development. These cities can also absorb the advantages of the cities in Levels 1 and 2, so as to achieve better development.
Levels 7 and 8 are the developing cities with negative comprehensive scores, and their overall development is below the average level. More aspects of development for the cities in these levels are lower than the average level. The economy and employment level for the cities in Level 7 lags behind. The cities in Level 8 only have one positive score of
Levels 9 to 15 are the cities to be developed. Their comprehensive scores are negative, and most common factor scores are also negative. Their development is considerably lower than the average level, and they all have weaknesses in development. All the scores of the common factors show negative for the cities in Levels 9, 11 and 12, showing that the overall development for these cities is below the average level and lag far behind. The values of
Through the above analysis, the conclusions are: (1) The main reasons for the differences in urban levels are the level and development of urban economy and employment, and these indicators play key roles in determining the level of the city; (2) There is a large difference in urban development between the top 20 cities and other cities; (3) The cities in each level have advantages and disadvantages in some aspects, and the overall development for the most cities is not very balanced; (4) Cities at the same level have similarities in urban development.
In this paper, based on multiple indicators of economy and industry, the urban hierarchical structure in China is investigated. The indicators from the aspects of economy, industry, infrastructure, medical care, population, education, culture and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure. The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China. According to the comprehensive scores, 285 cities above the prefecture level are clustered into 15 levels using the K-means clustering algorithm. Then, the hierarchical structure system of the cities above the prefecture level in China is obtained.
The conclusions can be drawn as follows: (1) The main reasons for the differences in urban levels are the level and development of urban economy and employment, and these indicators play key roles in determining the level of the city; (2) There is a large difference in urban development between the top 20 cities and other cities; (3) The cities in each level have advantages and disadvantages in some aspects, and the overall development for the most cities is not very balanced; (4) Cities at the same level have similarities in urban development.
Based on the conclusions, the policy implications are proposed as follows:
Firstly, although the factors of economy and employment are very important, the government also needs to focus on the development of industry, infrastructure, medical care, population, education and culture based on the economy and employment during the progress of urban development to achieve more balanced development.
Secondly, without changing the environment and inherent characteristics of the city, the government can strengthen the advantages, make up for the deficiencies and make the urban development more coordinated according to the quantitative data.
Thirdly, the needs of urban development at each level are different. The government can find out the gap during the development process of the city according to the backward aspects, and formulate a more complete and balanced urban planning of the city in each level.
Fourthly, the development of cities at all levels should consider their own specialized development direction on the basis of the national macro development strategy, maximize the advantages of the city, and formulate urban planning based on the characteristics of the city, combination with other industries and sustainable development, so as to make urban development more balanced.
This paper can identify the drawbacks during the urban development, help the government find out the gaps among the development process of economy, industry, infrastructure, medical care, population, education, culture and employment, and improve the overall development of the country.
The results and implications can not only be applied to the urban planning and development in China, but also offer a reference on other developing countries. Also, the methodologies used in this paper can be applied to study the urban hierarchical structure in other countries.